Today, it is common to migrate data from system to system so that critical business information can be extracted by relating data together (e.g. related financial data with manufacturing data, customer service data with installed inventory data, etc.). It is apparent that the current appetite/creativity to relate more and more disparate data domains is indirectly causing (using “traditional/familiar database technologies”) information technology (IT) costs to keep spiraling upwards. Using today's standard relational database technologies, and the exploding needs to relate disparate data groups/domains of information together, the IT cost of running an operation will inevitably and rapidly go up proportionally to the increased need for business intelligence needed to run the enterprise.
Under current approaches, data warehouses are confined to using a traditional relational database technology to store data. In the case of a virtual data mart, SQL views are used to create a “specialized” query. When using data warehouses or data marts, the operator has to move data from various sources. In every case, enterprises are forced to spend significant time and resources physically moving the data into a traditional relational database. Such a process can be frustratingly slow and cumbersome.
FIG. 1 shows a legacy method for accessing a database or series of databases 10A-B. Typical protocols used to interface with databases 10A-B are ODBC and JDBC, which allow for an application server 12 to access the Finite Bounded Persistent Memories (FBPMs) 14A-B of each separate database 10A-B. Some of the major limitations of this legacy method lie in the duplication of data that occurs as database operations are performed.
In view of the foregoing, there exists a need for an approach that solves at least one of the deficiencies in the related art.